Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives
Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
CNNs were extensively used in many aspects of medical image analysis, allowing for great …
Dual-branch residual network for lung nodule segmentation
H Cao, H Liu, E Song, CC Hung, G Ma, X Xu, R Jin… - Applied Soft …, 2020 - Elsevier
An accurate segmentation of lung nodules in computed tomography (CT) images is critical to
lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the …
lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the …
An efficient DA-net architecture for lung nodule segmentation
A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule
segmentation from computed tomography (CT) images becomes crucial for early lung …
segmentation from computed tomography (CT) images becomes crucial for early lung …
U-Det: A modified U-Net architecture with bidirectional feature network for lung nodule segmentation
NV Keetha, CSR Annavarapu - arXiv preprint arXiv:2003.09293, 2020 - arxiv.org
Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule
segmentation in computed tomography (CT) images. However, the anonymous shapes …
segmentation in computed tomography (CT) images. However, the anonymous shapes …
[HTML][HTML] Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction
Y Ni, Z Xie, D Zheng, Y Yang… - Quantitative Imaging in …, 2022 - ncbi.nlm.nih.gov
Background Accurate segmentation of pulmonary nodules is important for image-driven
nodule analysis and nodule malignancy risk prediction. However, due to interobserver …
nodule analysis and nodule malignancy risk prediction. However, due to interobserver …
An efficient U-Net framework for lung nodule detection using densely connected dilated convolutions
Remote health monitoring is an important aspect especially for remote locations where
standard medical facilities are not available. Smart cities use a similar concept to provide …
standard medical facilities are not available. Smart cities use a similar concept to provide …
Coarse-to-fine lung nodule segmentation in CT images with image enhancement and dual-branch network
Z Wu, Q Zhou, F Wang - Ieee Access, 2021 - ieeexplore.ieee.org
Lung nodule segmentation in CT images plays an important role in clinical diagnosis and
treatment of lung cancers. Among different types of nodules, the solitary nodules usually …
treatment of lung cancers. Among different types of nodules, the solitary nodules usually …
A Bi-FPN-based encoder–decoder model for lung nodule image segmentation
CSR Annavarapu, SAB Parisapogu, NV Keetha… - Diagnostics, 2023 - mdpi.com
Early detection and analysis of lung cancer involve a precise and efficient lung nodule
segmentation in computed tomography (CT) images. However, the anonymous shapes …
segmentation in computed tomography (CT) images. However, the anonymous shapes …
Pulmonary Nodule Segmentation using Deep Learning: A Review
Y Wang, SM Mustaza, MS Ab-Rahman - IEEE Access, 2024 - ieeexplore.ieee.org
Accurate segmentation of pulmonary nodule within medical imagery is of great significance
for classification and diagnosis. This task is profoundly challenging due to scarcity of …
for classification and diagnosis. This task is profoundly challenging due to scarcity of …
Multi-granularity scale-aware networks for hard pixels segmentation of pulmonary nodules
Accurate automatic segmentation of pulmonary nodules can greatly assist in the early
clinical diagnosis and analysis of lung cancer. However, it remains a challenging task due to …
clinical diagnosis and analysis of lung cancer. However, it remains a challenging task due to …